Table 1 Holistic innovation comparison across research dimensions.

From: Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect

Research dimension

Prior approach limitations (selected examples)

DIS-HARM innovation

Dynamic Adaptation

•Static carbon coins ([1])

•Fixed pricing ([2])

•Non-adaptive MARL ([3])

Meta-learning reward coefficients​ (Eq. 5): Real-time cₚ/cₒ updates

User Heterogeneity Modeling

• Linear VOT ([9])

• Discrete grouping ([10])

• Rigid probit models ([15])

​ηᵢ elasticity + Sigmoid βᵢ​ (Eq. 7,11): Nonlinear cross-modal response

Network Synergy

• Static maturity models ([18])

• TF-IDF recommendations ([20])

• Centrality-based assessment ([21])

Weather-modulated propagation​ (φ(wₜ) in Eq. 13): Dynamic influence decay

Multi-objective Optimization

• Efficiency-only focus ([3, 20])

• Unbalanced subsidies ([12])

​Fairness-efficiency trade-off​